A Statistical-Based Sequential Method for Fast Online Detection of Fault-Induced Voltage Dips

This paper addresses the problem of detecting voltage dips regarding measurements consisting of fault events, transformer saturation events, and capacitor-switching events. A novel statistical-based sequential detection method is proposed for online classification of these events. The detector is based on the Neyman–Pearson criterion that maximizes the detection rate of fault-induced dips with constrained false alarm rate of the other two types of event. The sequential detector is able to give an earliest possible event discrimination together with the estimated confidence at the time instant ranging from 1 8 1 4 1 2 to 3 4 cycle of the fundamental frequency after detecting an initial voltage drop at 0.95 p.u. The performance of the proposed scheme is evaluated using measurements from medium voltage networks.